Influence of HPLC Retention Data and Molecular Modeling Descriptors on Prediction of Pharmacological Classification of Drugs Using Principal Component Analysis Method

Abstract

The usage of principal component analysis (PCA) method in prediction of pharmacological classification of the drugs based on high-performance liquid chromatography (HPLC) retention data and on non-empirical structural parameters was studied. A group of 36 drugs of established pharmacological classification were chromatographed in ten carefully designed HPLC systems. Additionally, twelve structural descriptors were derived by molecular modeling studies based on the structural formula of considered drugs. A matrix of 36 x 22 HPLC data together with molecular properties parameters was subjected to chemometric analysis by PCA. Although that size of the training set could be sometimes disputable, the work remains as a demonstration of the basic methodology without the straight focus primarily intended asa report on a comprehensive predictive model. Nevertheless, the obtained clustering of drugs was in accordance with their pharmacological classification as well as chemical structures classification. The PCA method of the HPLC retention data and structural descriptors allowed to segregate drugs and drug candidates according to their pharmacological properties,and may be of potential help to limit the number of biological assays in the search for new drugs.

Assessment of Lipophilicity Descriptors of Selected NSAIDs Obtained at Different TLC Stationary Phases

“…The chromatographic behavior of drugs was studied on stationary phases: TLC silica gel 60 Merck. The standard solutions of compounds were prepared in methanol and applied in duplicate onto the plates by means of a 10 µL syringe (Hamilton Company, Bonaduz, Switzerland) in the form of 5 mm wide bands in increments of 5 mm.…”

“…Increasingly, thanks to computerized multidimensional data analysis procedures, it is possible to extract systematic information, often dispersed in large data sets. Upon application of chemometric methods (e.g., principal component analysis (PCA), cluster analysis (CA)), the number of variables in a data set is reduced by finding linear combinations of the variables that explain most of the data variability [60][61][62]. PCA allows for a more objective and rational estimation and comparison of the determined lipophilicity.…”

The comparison of semiempirical and ab initio molecular modeling methods in activity and property evaluation of selected antimicrobial sulfonamides

“…The chromatographic data for some sulfonamides come from works of Koba et al (2010) and Bober et al (2011) for XTerra RP-18, XTerra RP-8, IAM PC C10/C3, AGP, Hypersil HSA, Nucleosil 100-5 OH, Discovery HS PEG, IC Pak Anion HR, IC Pak Cation M/D, Spheri Anion AX and Purospher STAR RP-18, Aluspher RP select B, Chromolith RP-18, and Supelcosil Plus ABZ.…”

Pharmacological classification of anticancer drugs applying chromatographic retention data and chemometric analysis

“…Principal Component Analysis (PCA) is the most commonly used chemometric technique. Norinder et al have applied PCA, to extract the most important factors, further used to establish the regression equation in order to predict the enantioselectivity α, whereas Kumar et al have applied PCA for classifying aqueous herbal drugs as well as diagnosis and therapeutic prognosis of oral sub-mucous fibrosi (Bober et al 2011; (Koba and Baczek 2010;Koba and Baçzek 2012a, b;Koba et al 2010b;Norinder and Hermansson 1991;Stasiak et al 2010;Koba et al 2010a;Kumar 2017). Chemometric methods are also useful in case of increasing signal-to-noise ratio, removing undesired effects from data, or peak alignment.…”